Package: ensembleML 0.2.5

ensembleML: Unified Interface for Ensemble Machine Learning Methods

Provides a clean, unified interface for training, predicting, and evaluating ensemble machine learning models including Random Forest, Gradient Boosting ('XGBoost'), 'AdaBoost', and 'Bagging'. All algorithms share a consistent API: em_fit(), em_predict(), em_evaluate(), and em_tune(). Includes built-in cross-validation, feature importance, calibration diagnostics, partial dependence plots, and model comparison utilities. Methods: Breiman (2001) <doi:10.1023/A:1010933404324>; Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>; Freund and Schapire (1997) <doi:10.1006/jcss.1997.1504>; Breiman (1996) <doi:10.1007/BF00058655>.

Authors:Sadikul Islam [aut, cre]

ensembleML_0.2.5.tar.gz
ensembleML_0.2.5.zip(r-4.7-any)ensembleML_0.2.5.zip(r-4.6-any)ensembleML_0.2.5.zip(r-4.5-any)
ensembleML_0.2.5.tgz(r-4.6-any)ensembleML_0.2.5.tgz(r-4.5-any)
ensembleML_0.2.5.tar.gz(r-4.7-any)ensembleML_0.2.5.tar.gz(r-4.6-any)
ensembleML_0.2.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
ensembleML/json (API)

# Install 'ensembleML' in R:

install.packages('ensembleML', repos = c('https://sadikulislamiasri-hub.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 3 scripts 387 downloads 12 exports 83 dependencies

Last updated from:3d55983156. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	135
source / vignettes	OK	196
linux-release	OK	148
macos-release	OK	173
macos-oldrel	OK	172
windows-devel	OK	77
windows-release	OK	83
windows-oldrel	OK	77
wasm-release	OK	118

Exports:em_calibration em_compare em_confusion em_cv em_evaluate em_fit em_importance em_partial em_plot_cv em_predict em_residuals em_tune

Dependencies:adabag caret class cli clock codetools ConsRank cpp11 data.table diagram digest doParallel dplyr e1071 farver foreach future future.apply generics ggplot2 globals glue gower gtable gtools hardhat ipred isoband iterators jsonlite KernSmooth labeling lattice lava lifecycle listenv lubridate magrittr MASS Matrix ModelMetrics nlme nnet numDeriv parallelly pillar pkgconfig plyr pROC prodlim progressr proxy purrr R6 randomForest RColorBrewer Rcpp recipes reshape2 rlang rlist rpart S7 scales shape sparsevctrs SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tzdb utf8 vctrs viridisLite withr xgboost XML yaml

Getting Started with ensembleML

Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-06-05
Started: 2026-06-05

Help page	Topics
ensembleML: Unified Ensemble Machine Learning Interface	ensembleML-package ensembleML
Calibration (Reliability) Diagram	em_calibration
Compare Multiple Ensemble Algorithms	em_compare
Confusion Matrix	em_confusion
k-Fold Cross-Validation	em_cv
Evaluate Model Performance	em_evaluate
Fit an Ensemble Model	em_fit
Feature Importance	em_importance
Partial Dependence Plot	em_partial
Plot Cross-Validation Fold Results	em_plot_cv
Predict from an Ensemble Model	em_predict
Residual Diagnostics for Regression Models	em_residuals
Tune Hyperparameters via Cross-Validation Grid Search	em_tune

Package: ensembleML 0.2.5

ensembleML: Unified Interface for Ensemble Machine Learning Methods

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Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)